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Beyond Linear Attention: Softmax Transformers Implement In-Context Reinforcement Learning

arXiv:2605.07333v1 Announce Type: new Abstract: In-context reinforcement learning (ICRL) studies agents that, after pretraining, adapt to new tasks by conditioning on additional context without parame

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Beyond LoRA vs. Full Fine-Tuning: Gradient-Guided Optimizer Routing for LLM Adaptation

DGX agent

arXiv:2605.07111v1 Announce Type: cross Abstract: Recent literature on fine-tuning Large Language Models highlights a fundamental debate. While Full Fine-Tuning (FFT) provides the representational pla

model-releasesarxiv-cs-ai

11 May 2026

Beyond Pairs: Your Language Model is Secretly Optimizing a Preference Graph

arXiv:2605.08037v1 Announce Type: cross Abstract: Direct Preference Optimization (DPO) aligns language models using pairwise preference comparisons, offering a simple and effective alternative to Rein

safetyarxiv-cs-ai

Beyond Reasoning: Reinforcement Learning Unlocks Parametric Knowledge in LLMs

arXiv:2605.07153v1 Announce Type: new Abstract: Reinforcement learning (RL) has achieved remarkable success in LLM reasoning, but whether it can also improve direct recall of parametric knowledge rema

researcharxiv-cs-cl

Beyond Retrieval: A Multitask Benchmark and Model for Code Search

arXiv:2605.04615v2 Announce Type: replace-cross Abstract: Code search has usually been evaluated as first-stage retrieval, even though production systems rely on broader pipelines with reranking and d

Applications

Beyond Single Ground Truth: Reference Monism as Epistemic Injustice in ASR Evaluation

arXiv:2605.07084v1 Announce Type: new Abstract: Automatic speech recognition (ASR) evaluation compares system output to ground truth transcripts, with Word Error Rate (WER) quantifying the distance be

applicationsarxiv-cs-cl

Beyond State-Wise Mirror Descent: Offline Policy Optimization with Parametric Policies

arXiv:2602.23811v4 Announce Type: replace-cross Abstract: We investigate the theoretical aspects of offline reinforcement learning (RL) under general function approximation. While prior works (e.g., X

safetyarxiv-cs-ai

Beyond the Black Box: Interpretability of Agentic AI Tool Use

arXiv:2605.06890v1 Announce Type: new Abstract: AI agents are promising for high-stakes enterprise workflows, but dependable deployment remains limited because tool-use failures are difficult to diagn

Beyond the Wrapper: Identifying Artifact Reliance in Static Malware Classifiers using TRUSTEE

arXiv:2605.07034v1 Announce Type: cross Abstract: Modern cybersecurity relies heavily on static machine-learning-based malware classifiers. However, transformations such as packing and other non-seman

tutorialsarxiv-cs-lg

BGM-IV: an AI-powered Bayesian generative modeling approach for instrumental variable analysis

arXiv:2605.07029v1 Announce Type: cross Abstract: Instrumental-variable (IV) regression enables causal estimation under endogeneity, but modern IV problems often involve nonlinear structural effects a

Bi3: A Biplatform, Bicultural, Biperson Dataset for Social Robot Navigation

model-releasesarxiv-cs-ro

arXiv:2605.06863v1 Announce Type: new Abstract: We contribute Bi3, a dataset of social robot navigation among groups of people in a constrained lab space. Compared to prior data collection efforts for

Bias and Uncertainty in LLM-as-a-Judge Estimation

arXiv:2605.06939v1 Announce Type: new Abstract: LLM-as-a-Judge evaluation has become a standard tool for assessing base model performance. However, characterizing performance via the naive estimator,

safetyarxiv-cs-lg

Bifurcation Models: Learning Set-Valued Solution Maps with Weight-Tied Dynamics

arXiv:2605.07277v1 Announce Type: cross Abstract: Many scientific and combinatorial problems admit multiple correct solutions, not a single label. Standard supervised learning resolves this ambiguity

tutorialsarxiv-cs-ai

Bilevel Graph Structure Learning, Revisited: Inner-Channel Origins of the Reported Gain

arXiv:2605.07577v1 Announce Type: new Abstract: Bilevel graph structure learning is widely understood to improve graph neural networks by jointly optimizing model parameters and a learned graph struct

BioProVLA-Agent: An Affordable, Protocol-Driven, Vision-Enhanced VLA-Enabled Embodied Multi-Agent System with Closed-Loop-Capable Reasoning for Biological Laboratory Manipulation

arXiv:2605.07306v1 Announce Type: cross Abstract: Biological laboratory automation can reduce repetitive manual work and improve reproducibility, but reliable embodied execution in wet-lab environment

bispectrum: Selective G-Bispectra Made Practical

arXiv:2605.07270v1 Announce Type: new Abstract: Many machine learning tasks are invariant under the action of a group G of transformations: signal classification can be invariant under translations, i

Black-box model classification under the discriminative factorization

arXiv:2605.07878v1 Announce Type: new Abstract: Access to modern generative systems is often restricted to querying an API (the ``black-box' setting) and many properties of the system are unknown to t

Bloom Filter Encoding for Machine Learning

arXiv:2512.19991v2 Announce Type: replace Abstract: We present a method that uses a Bloom filter transform to preprocess data for machine learning. Each sample is encoded into a compact bit-array repr

Agents

Bluetooth Phased-array Aided Inertial Navigation Using Factor Graphs: Experimental Verification

arXiv:2602.17407v2 Announce Type: replace-cross Abstract: Phased-array Bluetooth systems have emerged as a low-cost alternative for performing aided inertial navigation in GNSS-denied use cases such a

agentsarxiv-cs-ro

BoHA: Blockwise Hadamard Product Adaptation for Parameter-Efficient Fine-Tuning

arXiv:2509.21637v2 Announce Type: replace Abstract: Parameter-efficient fine-tuning (PEFT) of large language models trains a small task-specific parameter set while keeping the pretrained model frozen

Bounded Fitting for Expressive Description Logics

arXiv:2605.07452v1 Announce Type: new Abstract: Bounded fitting is an attractive paradigm for learning logical formulas from labeled data examples that offers PAC-style generalization guarantees and c

researcharxiv-cs-ai

🚨BREAKING: Ilya just confirmed under oath what he saw: Sam lying. And he confirmed that he thought it was appropriate to fire Altman for it…

🚨BREAKING: Ilya just confirmed under oath what he saw: Sam lying. And he confirmed that he thought it was appropriate to fire Altman for it. And that he had been concerned for about Sam for a long tim

safetygary-marcus--x

Breaking QAOA's Fixed Target Hamiltonian Barrier: A Fully Connected Quantum Boltzmann Machine via Bilevel Optimization

arXiv:2605.07473v1 Announce Type: cross Abstract: To overcome the limitations of classical partially connected Boltzmann machines and mainstream quantum Boltzmann machines (QBMs), this work extends th

Breaking Spatial Uniformity: Prior-Guided Mamba with Radial Serialization for Lens Flare Removal

model-releasesarxiv-cs-cv

arXiv:2605.07650v1 Announce Type: new Abstract: Lens flares, caused by complex optical aberrations, severely degrade image quality especially in nighttime photography. Although recent restoration meth

Breaking the Illusion: When Positive Meets Negative in Multimodal Decoding

arXiv:2605.06679v1 Announce Type: new Abstract: Vision-Language Models (VLMs) are frequently undermined by object hallucination, generating content that contradicts visual reality, due to an over-reli

Agents

BrickCraft: Visuomotor Skill Composition with Situated Manual Guidance for Long-Horizon Interlocking Brick Assembly

arXiv:2605.07605v1 Announce Type: new Abstract: Autonomous robotic assembly of interlocking bricks demands seamless integration of long-horizon task reasoning, spatial grounding, and fine-grained mani

agentsarxiv-cs-ro

BRIDGE: Background Routing and Isolated Discrete Gating for Coarse-Mask Local Editing

model-releasesarxiv-cs-cv

arXiv:2605.07846v1 Announce Type: new Abstract: Coarse-mask local image editing asks a model to modify a user-indicated region while preserving the surrounding scene. In practice, however, rough masks

Bridging Textual Profiles and Latent User Embeddings for Personalization

arXiv:2605.06981v1 Announce Type: cross Abstract: Personalized systems rely on user representations to connect behavioral history with downstream recommendation applications. Existing methods typicall

researcharxiv-cs-cl

Bridging the Last Mile of Circuit Design: PostEDA-Bench, a Hierarchical Benchmark for PPA Convergence and DRC Fixing

arXiv:2605.06936v1 Announce Type: cross Abstract: LLM-based agents are increasingly applied to the 'last mile' of Electronic Design Automation (EDA): repairing residual sign-off Design Rule Check (DRC

Bringing Multimodal Large Language Models to Infrared-Visible Image Fusion Quality Assessment

arXiv:2605.06969v1 Announce Type: new Abstract: Infrared-Visible image fusion (IVIF) aims to integrate thermal information and detailed spatial structures into a single fused image to enhance percepti

tutorialsarxiv-cs-cv

Tools

Building Blocks for Foundation Model Training and Inference on AWS

This article discusses AWS infrastructure, tools, and services designed to support the training and deployment of large foundation models, enabling machine learning practitioners to leverage AWS's com

toolshugging-face

Building web search-enabled agents with Strands and Exa

In this post, you will learn how to set up the Exa integration in Strands Agents, understand the two core tools it exposes, and walk through real-world use cases that show how agents use web search to

tutorialsaws-ml-blog

CA-SQL: Complexity-Aware Inference Time Reasoning for Text-to-SQL via Exploration and Compute Budget Allocation

arXiv:2605.08057v1 Announce Type: cross Abstract: While recent advancements in inference-time learning have improved LLM reasoning on Text-to-SQL tasks, current solutions still struggle to perform wel

CalexNet: Soft Cascade-Aligned Training and Calibration for Lightweight Early-Exit Branches

arXiv:2509.08318v2 Announce Type: replace Abstract: Early-exit cascades over a frozen convolutional backbone enable adaptive inference but suffer from three sources of train-inference mismatch: branch

safetyarxiv-cs-cv

Can Agents Price a Reaction? Evaluating LLMs on Chemical Cost Reasoning

arXiv:2605.07251v1 Announce Type: new Abstract: Large Language Models (LLMs) have become increasingly capable as tool-using agents, with benchmarks spanning diverse general agentic tasks. Yet rigorous

Can David Beat Goliath? On Multi-Hop Reasoning with Resource-Constrained Agents

arXiv:2601.21699v2 Announce Type: replace Abstract: Multi-turn reasoning agents solve complex questions by decomposing them into intermediate retrieval or tool-use steps, for accumulating supporting e

safetyarxiv-cs-cl

Applications

Can LLMs Take Retrieved Information with a Grain of Salt?

arXiv:2605.06919v1 Announce Type: new Abstract: Large language models have demonstrated impressive retrieval-augmented capabilities. However, a crucial area remains underexplored: their ability to app

applicationsarxiv-cs-cl

Can You Break RLVER? Probing Adversarial Robustness of RL-Trained Empathetic Agents

arXiv:2605.07138v1 Announce Type: new Abstract: Reinforcement learning from verifiable emotion rewards RLVER has produced language models with strong empathetic performance, evaluated on benchmarks th

CarCrashNet: A Large-Scale Dataset and Hierarchical Neural Solver for Data-Driven Structural Crash Simulation

arXiv:2605.07098v1 Announce Type: new Abstract: Crash simulation is a cornerstone of modern vehicle development because it reduces the need for costly physical prototypes, accelerates safety-driven de

CARMEN: CORDIC-Accelerated Resource-Efficient Multi-Precision Inference Engine for Deep Learning

arXiv:2605.06878v1 Announce Type: cross Abstract: This paper presents CARMEN, a runtime-adaptive, CORDIC-accelerated multi-precision vector engine for resource-efficient deep learning inference. The k

researcharxiv-cs-ro

Agents

CASCADE: Case-Based Continual Adaptation for Large Language Models During Deployment

arXiv:2605.06702v1 Announce Type: new Abstract: Large language models (LLMs) have become a central foundation of modern artificial intelligence, yet their lifecycle remains constrained by a rigid sepa

agentsarxiv-cs-ai

CASCADE: Context-Aware Relaxation for Speculative Image Decoding

arXiv:2605.07230v1 Announce Type: cross Abstract: Autoregressive generation is a powerful approach for high-fidelity image synthesis, but it remains computationally demanding and slow even on the most

researcharxiv-cs-ai

Applications

Causal-Aware Foundation-Model for Bilevel Optimization in Discrete Choice Settings

arXiv:2605.06941v1 Announce Type: new Abstract: We introduce a causal aware foundation-model framework for real time optimal decision making in discrete choice environments. We propose a constrained t

applicationsarxiv-cs-lg

Causal EpiNets: Precision-corrected Bounds on Individual Treatment Effects using Epistemic Neural Networks

arXiv:2605.07065v1 Announce Type: cross Abstract: Individual treatment effects are not point-identified from data. The Probability of Necessity and Sufficiency (PNS) circumvents this limitation by cha

safetyarxiv-cs-ai

CellScientist: Dual-Space Hierarchical Orchestration for Closed-Loop Refinement of Virtual Cell Models

arXiv:2605.07335v1 Announce Type: new Abstract: Virtual Cell Modeling (VCM) requires models that not only predict perturbation responses, but also support targeted revision when predictions fail. Curr

CGF-Softmax: A Cumulant-Based Softmax Reformulation for Efficient Inference under Homomorphic Encryption

arXiv:2602.01621v3 Announce Type: replace-cross Abstract: Homomorphic encryption (HE) is a prominent framework for privacy-preserving machine learning, enabling inference directly on encrypted data. H

Chain-based Distillation for Effective Initialization of Variable-Sized Small Language Models

model-releasesarxiv-cs-cl

arXiv:2605.07783v1 Announce Type: new Abstract: Large language models (LLMs) achieve strong performance but remain costly to deploy in resource-constrained settings. Training small language models (SL

Characterizing and Correcting Effective Target Shift in Online Learning

arXiv:2605.07886v1 Announce Type: cross Abstract: Online learning from a stream of data is a defining feature of intelligence, yet modern machine learning systems often struggle in this setting, espec

tutorialsarxiv-cs-lg